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      <title>Java智能体打造企业级自动化工作流 - 交互式测验</title>
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    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>Java智能体打造企业级自动化工作流 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
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                    <div id="question-container">
                      <p id="question-text"></p>
                      <ul class="options-list" id="options-list"></ul>
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                    <div id="result-container"></div>
                    <div id="hint-container" class="hint-container"></div>
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                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
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      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档，智能代理（Intelligent Agent）的基本工作原理遵循哪个核心循环？",
    "options": [
      "输入-处理-输出 (Input-Process-Output)",
      "数据-训练-预测 (Data-Train-Predict)",
      "感知-思考-行动 (Perceive-Think-Act)",
      "设计-编码-测试 (Design-Code-Test)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出，智能代理的基本工作原理遵循“感知-思考-行动”循环。它首先通过“感知”从环境中收集信息，然后通过“思考”进行分析决策，最后通过“行动”执行具体操作。其他选项描述的是通用的数据处理、机器学习或软件开发流程，而非智能代理的特定工作原理。",
    "source": "文档中「一、智能代理技术基础与Java优势」->「1. 智能代理的定义与工作原理」部分",
    "hint": "文档中提到智能代理是一种能够感知环境并采取行动的软件实体，其工作原理被概括为一个三步循环。"
  },
  {
    "question": "在对比智能代理的两种主要决策机制时，文档中的Drools规则引擎最适合处理哪类问题？",
    "options": [
      "基于历史数据预测未来销售趋势",
      "处理明确、预定义的、复杂的业务逻辑（如VIP客户订单优先）",
      "从大量非结构化文本中分析用户意图",
      "优化资源分配以实现全局最优解"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档将规则引擎（以Drools为例）与机器学习模型进行了对比。规则引擎的核心优势在于处理那些可以通过明确的“when-then”（如果-那么）逻辑来描述的复杂业务规则。示例“高价值客户订单优先处理”就是典型的应用场景。而预测、意图分析和优化问题通常更适合使用机器学习模型。",
    "source": "文档中「二、构建智能代理的核心组件」->「2. 规则引擎vs机器学习模型」部分",
    "hint": "可以参考文档中给出的规则文件示例，思考它解决的是哪一类问题。"
  },
  {
    "question": "在“实时交易监控与风险预警”的实战案例中，`FraudDetectionAgent`做出决策的关键依据是什么？",
    "options": [
      "交易金额是否超过一个固定的阈值",
      "通过调用NLP服务分析交易备注的文本内容",
      "基于交易信息和客户历史行为计算出的一个动态风险分数",
      "是否收到了来自外部系统的欺诈警报"
    ],
    "correctAnswerIndex": 2,
    "explanation": "代码示例显示，`FraudDetectionAgent`的`think`方法会调用`riskEngine.calculateRiskScore`方法，该方法结合了当前交易和客户的历史画像（CustomerProfile）来计算一个风险分数。代理随后根据这个分数的高低（例如大于0.8）来决定是否阻止交易，这是一种比固定阈值更智能、更动态的决策方式。",
    "source": "文档中「三、Java智能代理实战案例」->「3. 实时交易监控与风险预警」部分",
    "hint": "在`FraudDetectionAgent`的`think`方法中，有一个关键的计算步骤决定了后续的行动。"
  },
  {
    "question": "文档中提到的分布式智能代理架构中，`AgentCoordinator`组件的核心职责是什么？",
    "options": [
      "执行具体的业务操作，如审批订单或发送通知",
      "缓存常用数据以提高单个代理的决策速度",
      "作为消息队列，负责在不同代理之间传递消息",
      "分解复杂任务，将其分配给合适的代理，并聚合最终结果"
    ],
    "correctAnswerIndex": 3,
    "explanation": "根据“分布式智能代理架构设计”一节的描述和代码，`AgentCoordinator`扮演着协调者的角色。它的`coordinateTask`方法清晰地展示了其职责：任务分解（decompose）、任务分配（assignTask）和结果聚合（addResult），从而协调多个分布式代理共同完成一个大型任务。",
    "source": "文档中「四、高级特性实现技巧」->「1. 分布式智能代理架构设计」部分",
    "hint": "思考一下“Coordinator”（协调者）这个词的含义，它在多成员协作中通常扮演什么角色？"
  },
  {
    "question": "为了提升智能代理的性能，文档介绍了多种技巧。其中，使用`CompletableFuture`和`ExecutorService`主要解决了什么问题？",
    "options": [
      "通过缓存避免对相同输入的重复计算",
      "通过AOP监控方法的执行时间以发现瓶颈",
      "利用多线程并行处理多个任务，提高系统吞吐量",
      "减少因频繁数据库查询导致的I/O延迟"
    ],
    "correctAnswerIndex": 2,
    "explanation": "在“多线程与异步处理技巧”一节中，`ParallelDecisionEngine`的例子明确使用了`CompletableFuture.supplyAsync`和`ExecutorService`。这种设计的目的是将一系列任务提交到线程池中并行执行，从而有效利用多核CPU资源，提高系统的整体处理能力和吞吐量，而不是针对单个重复任务的加速。",
    "source": "文档中「五、性能优化与扩展性保障」->「2. 多线程与异步处理技巧」部分",
    "hint": "该技巧的标题是“多线程与异步处理”，这通常与并发执行任务有关。"
  },
  {
    "question": "根据文档中`DataCollector`的代码示例，以下哪一项**不是**智能代理获取环境信息的方式？",
    "options": [
      "监听Kafka消息队列",
      "调用REST API接口",
      "直接读取用户的键盘输入",
      "从数据库中执行查询"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中的`DataCollector`类展示了多种企业级的数据获取方式，包括`collectFromDatabase`（数据库）、`collectFromApi`（API调用）和`collectFromKafka`（消息队列）。代码中并未提及或展示通过命令行直接读取用户键盘输入的方式，这在通常的企业级自动化代理中也很少见。",
    "source": "文档中「二、构建智能代理的核心组件」->「1. 如何获取业务数据与环境信息」部分",
    "hint": "请检查`DataCollector`类中的方法名，看看它们分别对应了哪些数据源。"
  },
  {
    "question": "文档中“让代理不断进化”一节提出了学习机制。当代理的决策导致了不理想的结果时，`LearningAgent`会执行什么关键操作？",
    "options": [
      "立即停止运行并向管理员发送警报",
      "将该决策从缓存中清除",
      "调用`learner.adjustModel`来调整其决策模型",
      "将任务重新分配给另一个代理"
    ],
    "correctAnswerIndex": 2,
    "explanation": "在`LearningAgent`的`learn`方法中，代码逻辑明确指出：`if (!result.isSuccessful()) { learner.adjustModel(env, decision, result); }`。这表明当结果不成功时，代理会触发学习者的`adjustModel`方法，利用这次失败的经验来调整或优化其内部的决策模型，从而实现“从错误中学习”并不断进化。",
    "source": "文档中「二、构建智能代理的核心组件」->「4. 让代理不断进化」部分",
    "hint": "在`LearningAgent`类的`learn`方法中，有一个条件判断语句专门处理当`result.isSuccessful()`为`false`时的情况。"
  }
];
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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
                '</div>' +
                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  